Oil Market Efficiency Under a Machine Learning Perspective
18 Pages Posted: 26 Nov 2018
Date Written: September 15, 2018
Forecasting commodities and especially oil prices has attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables often used in the relevant literature and through a selection process we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.
Keywords: Oil Prices, Forecasting, Machine Learning, Support Vector Machines
JEL Classification: C22, C53
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